Mobility Analytics Blog

StreetLight Data Blog

The latest news about Big Data and mobility analytics.

Blog Feature

Big Data  |  Case Studies  |  Transportation

Minnesota Department of Transportation Taps StreetLight Data to Bring New Traffic Intelligence to Everything From the Super Bowl to the Minnesota State Fair

We’re excited to share that StreetLight Data has a new public agency partner: Minnesota Department of Transportation (MnDOT). The agency recently signed up for a one-year pilot of our Regional Subscription to StreetLight InSight®, the first online platform that turns Big Data from mobile devices into transportation Metrics.

MnDOT’s Regional Subscription provides designated users with unlimited access to StreetLight InSight for Metrics in the state of Minnesota (and a buffer area). That means MnDOT’s Regional Subscription users can design and run as many StreetLight InSight transportation studies as desired to during their subscription term – without any incremental costs or additional procurement processes.

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Blog Feature

Big Data  |  Case Studies  |  Transportation

Ohio DOT Selects StreetLight Data and INRIX for On-Demand Mobility Intelligence

Ohio Department of Transportation (ODOT) recently selected StreetLight Data to provide on-demand transportation studies along with one of our partners, INRIX. We’re thrilled to see ODOT join the hundreds of public agencies across the US and Canada that benefit from our Big Data analytics. 

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Blog Feature

Big Data  |  Case Studies  |  Transportation

Transportation Demand Management in the (Always Changing) Real World

From ride-hailing apps and volatile gas prices to electric cars and (theoretically) autonomous vehicles, transportation behavior is rapidly changing. To properly plan for and manage our evolving transportation system, engineers and planners must keep pace with these changes. If managing transportation demand is important to your community, it’s not enough to follow the old pattern of creating new core analytics every 5-10 years to feed your models for any type of planning. For transportation demand management (TDM), which could be most profoundly affected by these new trends, the need for up-to-date, real, accurate data is even sharper.

In today’s evolving environment, effective TDM requires regular access to clean, up-to-date data. One problem that many planners face is that surveys and other traditional data-gathering methods simply cannot deliver high quality data at a frequent update cadence and affordable price.

Keep reading this blog post to learn all about TDM, and to find out how Big Data can help you maximize the impact of TDM strategies.

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Blog Feature

Big Data  |  Case Studies  |  Performance Measurement  |  Transportation

Measuring Big Data’s Impact: Siemens SCOOT Adaptive Signal Control Technology

Note: This is a guest blog post from Wendy Tao, the Head of Business Development and Strategy of the Intelligent Transportation Systems Group at Siemens Mobility. Wendy helps communities develop Smart Cities solutions related to advanced traffic management systems, adaptive signal control, connected vehicles and multi-modal applications.

From Intelligent Transportation Systems (ITS) to Massive Mobile Data, innovative technologies are tackling decades old challenges and creating new opportunities in the transportation industry.  And it’s not just an idea. We’re seeing significant impacts derived from in-depth evaluations on project performance and cost-effectiveness. Siemens recently partnered with StreetLight Data to measure the impact of a Siemens’ SCOOT adaptive signal control implementation in Ann Arbor, MI. Our empirical before-and-after study showed that SCOOT can reduce travel times by 10 to 20 percent. The study used archival navigation-GPS data from connected cars.

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Blog Feature

Big Data  |  Case Studies  |  Traffic  |  Transportation

Going Beyond Stationary Sensors to Understand Traffic in New York

The smart city movement’s first wave brought tons of stationary sensors to our cities, especially in the context of transportation. These sensors are passively collecting valuable travel pattern information at traffic lights, parking lots, bus stops, sidewalks, and more. But if we want cities that are truly smart – if we want to solve the challenges exposed by our stationary sensors – we have to go beyond them. In this blog post, I will use New York City as a case study to explain why.

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Blog Feature

Case Studies

[CASE STUDY] Integrated Analysis to Analyze Viewership

This analysis was done in conjunction with our friends at MotionLoft. Thanks to them and to the team at Great Wall of Oakland. Also thanks to Ozumo restaurant and The Broadway Grand apartments for donating the location and powersource for the Motionloft sensors.

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